This work combines knowledge engineering, based upon clinical experience, with mathematical methods and information theory, to process and analyze semi-automatically electrocardiographic data with the object of improving diagnostic accuracy and/or prognostic power. Electrocardiography is still used more than any other single diagnostic tool in cardiology. Past applications of this work involved single, resting routine ECGs in epidemiological studies of the Framingham population. However, such single routine ECGs, which are useful in revealing current status of subjects, do not reveal dynamic changes occurring in minutes to hours. This work has been redirected to monitoring ECGs which can be recorded in different situations, i.e.: i) the intensive care unit, ii) the 24-hour ambulatory recording (AECG), and iii) recording in a cardiology laboratory The outcome from monitoring is critically dependent upon the fidelity of th signal and the completeness of huge streams of data in such contexts. The present focus of this work combines analysis of the ECG with other continuous cardiovascular data (blood pressure in the radial artery and blood flow via pulse oximeter) to resolve pathophysiological mechanisms of syncope in young patients, and interpret their responses to pharmaceutical intervention as a guide to their clinical management.